In the file, we’re going try playing around with a couple of datasets. We’ll start with Gapminder like we did in class, and then move onto some of the built-in datasets in R that are available to us.
We’re going to start with the Gapminder data frame to try out some R functions.
head(gapminder)
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
So it looks like we have a bunch of data on some countries. Let’s see if we have anything on our favorite countries, Canada and the US.
any(gapminder=="Canada")
## [1] TRUE
any(gapminder=="United States")
## [1] TRUE
Great, now lets compare the two in a nice plot.
Well, I was hoping Canada would do better than the States in this category but I guess not. Let’s move on, shall we?
There’s plenty of built-in datasets meant just for this purpose - getting used to R. Because I found out how to do it on the internet, let’s plot the surface graph of a volcano!
## Loading required package: ggplot2
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout